Certain classes of routines can be characterized by repetitive events occurring with a periodic frequency or temporal regularity, known as rhythm. Patterns can be identified from the events to characterize a routine task performed by an individual at home, in the workplace, or during a social event. In the workplace, patterns provide valuable information for scheduling purposes and work efficiency. For example, the regular rotation of doctors while taking their hospital rounds generally follows a pattern with predictable regularity. The pattern can include events, such as visiting patients and family members, stopping for coffee, and restroom breaks. Once nurses are aware of a pattern, the nurses can determine the best time to contact a doctor, rather than waste resources and time waiting for the doctor. Routine tasks, such as the doctor's rotation, are characterized by specific recurrent events that are executed within nearly constant time intervals. By examining past, recurring rhythms, one can predict a task based on current events.
More recently, usage of certain media, such as email, has been observed to have rhythms. Conventional task management systems provide efficient methods for task switching, resumption, and identification. However, the conventional task management systems fail to use temporal information for describing and detecting routine tasks. For example, the TaskTracer system, which is a part of the Cognitive Assistant that Learns and Organizes project by the Defense Advanced Research Projects Agency, focuses on determining how tasks are completed. A user's interactions with respect to computer applications are tracked. The interactions are then organized according to task. The task information is used to increase efficiency and productivity in a work environment. However, temporal information, including identifying durations between interactions is not considered.
Additionally, the Semantic Analysis of Window Titles and Switching History system (“SWISH”) by Microsoft Corporation, Redmond, Wash., attempts to automatically detect tasks through window switching analysis by identifying windows on a user's desktop and determining a relationship between the windows. Windows that belong to the same task are assumed to share common properties, which are indicated by the relationship. The windows switching analysis of SWISH focuses on building a connected graph of window switch sequences within a predefined time interval, rather than characterizing a task using duration information between each window switch.
Thus, a system and method for describing and detecting routine tasks using temporal measures of user events is needed.